Computer Axial Tomography (CAT), sometimes known generally as Computerized Tomography (CT), is used in many applications, especially medical radiology, to obtain two or three dimensional views of the interior of three dimensional bodies (CT or CAT Scans). The technique involves subjecting a three dimensional body to radiation that enters the body from many different angles. The amount of radiation that is scattered or reflected by the body is then detected as a function of the angle of scattering. The scattered data is then analyzed to construct an image of the interior of the body. A two dimensional “slice” of the interior can be “reconstructed”, for example on a screen, and viewed. The slice can be reconstructed for any desired angle of intersection with the body.
While computerized tomography is well known, various specific mathematical reconstruction algorithms have been proposed to construct the image from the scattered radiation. However, it is becoming more challenging for known conventional reconstruction methods to meet the stringent constraints of current imaging applications. For example, the rates at which the impinging radiation beam scans the body has increased dramatically over the years, and the impinging radiation dosage has dropped significantly, especially in medical applications, because of patient safety concerns.
One area in which CT scans can be used is to detect lung pathology (i.e., disease in one or both lungs). Typically, the lungs are segmented from a CT scan in order to isolate the lungs from the rest of the image. Lung segmentation is typically a primary step for applications such as lung nodule detection and segmentation (i.e., the detection of and segmentation of masses of tissues in the lung), lung registration (i.e., automatic computation of the independent transformation of the right or left lung from one dataset to another from the same patient. The datasets are typically acquired at two different time points), volumetric analysis and pathology analysis such as emphysema detection, etc.
FIG. 1 shows a prior art 2-D image 100 before segmentation. The image includes a background area 104, a chest wall 108 (consisting of ribs and muscles), a right lung 112, and a left lung 116 The intensity level of the lungs is lower than that of the surrounding anatomies such as bones, muscles, and fat. A typical goal of lung segmentation is to extract the left and right lungs 112, 116 from the image 100.
A number of methods have been developed to extract the lung regions from a CT image (also referred to as a volume). Some methods are semi-automatic and involve user guidance. These methods often require a physician to designate a seed point inside the CT image. From this seed point, a lung region is grown. The image may then be thresholded. Thresholding an image occurs when pixels of the image that have a grey level higher (or lower) than a predetermined value, or threshold, is designated as being of interest, and the remaining pixels are designated as not being of interest. A histogram of the grey levels of the image may be used to determine a threshold for the lungs within the image. The results of these semi-automatic methods can be unsatisfactory and may require further manual corrections (e.g., because of the presence of other regions of similar density. This could bias the value of the automated threshold and, as a result, the lung may be over or under grown).
Automatic lung segmentation techniques have also been developed. Typically, lung regions are separated from their surrounding tissues based on a threshold. The threshold can be predefined from empirical results and can be determined dynamically at run-time based on image histograms (as described above). An iterative method may also be used to find a threshold.
In these techniques, algorithm speed is often affected by region-growing of the lungs. In particular, the growing of a lung region from a seed point is often computationally intensive and, as a result, the time required to perform the region growing is traditionally long relative to other operations.
Additionally, sometimes masses of tissues in the lung (i.e., lung nodules) are not included in a segmented lung region. In particular, some nodules touch the chest wall and possess the same intensity level as the chest wall. After thresholding is performed on the image to extract the lung regions, the nodules may be excluded from the extracted lung regions. Further, lung nodules typically cannot be correctly recovered by a set of morphological operations. As a result, lung nodules are often improperly excluded from segmented lungs.
Therefore, there remains a need to more efficiently segment lung regions from a CT image and further to accurately segment a lung having lung nodules.